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Apache Spark vs Azure HDInsight: What are the differences?
Distribution and Scalability: Apache Spark is a distributed processing system that allows users to process large-scale datasets in parallel. It is designed to be highly scalable and can handle workloads on clusters of thousands of nodes. Azure HDInsight, on the other hand, is a fully-managed cloud service that provides Apache Hadoop, Spark, and other big data processing frameworks. It leverages the scalability of the Azure cloud platform to handle large-scale data processing tasks.
Ease of Use and Flexibility: Spark provides a user-friendly API that allows developers to write applications in multiple languages such as Scala, Java, Python, and R. It offers a rich set of libraries and tools for data analytics, machine learning, and graph processing. Azure HDInsight, being a managed service, simplifies the deployment and management of Spark clusters. It integrates well with other Azure services and provides an intuitive user interface for managing and monitoring Spark jobs.
Integration with Azure Services: HDInsight provides tight integration with other Azure services such as Azure Storage, Azure Data Lake Storage, Azure Active Directory, and Azure SQL Database. This enables users to easily ingest, store, and analyze data from various sources within the Azure ecosystem. Spark can seamlessly read and write data to/from these Azure services, making it easier to build end-to-end data pipelines.
Advanced Analytics and Machine Learning: Spark has built-in support for advanced analytics and machine learning through its MLlib library. It provides a wide range of algorithms for classification, regression, clustering, and recommendation. Azure HDInsight extends Spark's machine learning capabilities by integrating with other Azure services such as Azure Machine Learning and Azure Databricks. This allows users to leverage the power of these services for building and deploying advanced ML models at scale.
Security and Compliance: HDInsight provides robust security features such as role-based access control (RBAC), Azure Active Directory integration, network isolation, and encryption at rest. It also helps organizations meet compliance requirements by supporting data governance frameworks like GDPR, HIPAA, and ISO 27001. Spark, on the other hand, provides fine-grained security controls through features like authentication, authorization, and encryption. It can integrate with external systems for user authentication and access control.
Pricing and Cost Optimization: Apache Spark is an open-source framework and can be used for free. However, the cost of deploying, configuring, and managing Spark clusters can add up for organizations. Azure HDInsight provides a pay-as-you-go pricing model, allowing users to optimize costs by scaling clusters up or down based on workload demands. It also offers cost management features like automatic scaling, cluster resizing, and instance type selection to ensure efficient resource utilization.
In Summary, Apache Spark and Azure HDInsight differ in terms of distribution and scalability, ease of use and flexibility, integration with Azure services, advanced analytics and machine learning capabilities, security and compliance features, and pricing and cost optimization.
We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.
In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.
In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
Pros of Azure HDInsight
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
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Cons of Azure HDInsight
Cons of Apache Spark
- Speed4